OpenRaiser/Intern-Atlas huggingface dataset:
A Comprehensive Perspective on Explainable AI across the Machine Learning Workflow
Venue: arXiv cs.LG 2025
Year: 2025

Artificial intelligence is reshaping science and industry, yet many users still regard its models as opaque "black boxes". Conventional explainable artificial-intelligence methods clarify individual predictions but overlook the upstream decisions and downstream quality checks that determine whether insights can be trusted. In this work, we present Holistic Explainable Artificial Intelligence (HXAI), a user-centric framework that embeds explanation into every stage of the data-analysis workflow and tailors those explanations to users. HXAI unifies six components (data, analysis set-up, learning process, model output, model quality, communication channel) into a single taxonomy and aligns each component with the needs of domain experts, data analysts and data scientists. A 112-item question bank covers these needs; our survey of contemporary tools highlights critical coverage gaps. Grounded in theories of human explanation, principles from human-computer interaction and findings from empirical user studies, HXAI identifies the characteristics that make explanations clear, actionable and cognitively manageable. A comprehensive taxonomy operationalises these insights, reducing terminological ambiguity and enabling rigorous coverage analysis of existing toolchains. We further demonstrate how AI agents that embed large-language models can orchestrate diverse explanation techniques, translating technical artifacts into stakeholder-specific narratives that bridge the gap between AI developers and domain experts. Departing from traditional surveys or perspective articles, this work melds concepts from multiple disciplines, lessons from real-world projects and a critical synthesis of the literature to advance a novel, end-to-end viewpoint on transparency, trustworthiness and responsible AI deployment.
DFed-SST: Building Semantic- and Structure-aware Topologies for Decentralized Federated Graph Learning
Venue: arXiv cs.LG 2025
Year: 2025

Decentralized Federated Learning (DFL) has emerged as a robust distributed paradigm that circumvents the single-point-of-failure and communication bottleneck risks of centralized architectures. However, a significant challenge arises as existing DFL optimization strategies, primarily designed for tasks such as computer vision, fail to address the unique topological information inherent in the local subgraph. Notably, while Federated Graph Learning (FGL) is tailored for graph data, it is predominantly implemented in a centralized server-client model, failing to leverage the benefits of decentralization.To bridge this gap, we propose DFed-SST, a decentralized federated graph learning framework with adaptive communication. The core of our method is a dual-topology adaptive communication mechanism that leverages the unique topological features of each client's local subgraph to dynamically construct and optimize the inter-client communication topology. This allows our framework to guide model aggregation efficiently in the face of heterogeneity. Extensive experiments on eight real-world datasets consistently demonstrate the superiority of DFed-SST, achieving 3.26% improvement in average accuracy over baseline methods.
Language models align with brain regions that represent concepts across modalities
Venue: arXiv cs.CL 2025
Year: 2025

Cognitive science and neuroscience have long faced the challenge of disentangling representations of language from representations of conceptual meaning. As the same problem arises in today's language models (LMs), we investigate the relationship between LM--brain alignment and two neural metrics: (1) the level of brain activation during processing of sentences, targeting linguistic processing, and (2) a novel measure of meaning consistency across input modalities, which quantifies how consistently a brain region responds to the same concept across paradigms (sentence, word cloud, image) using an fMRI dataset (Pereira et al., 2018). Our experiments show that both language-only and language-vision models predict the signal better in more meaning-consistent areas of the brain, even when these areas are not strongly sensitive to language processing, suggesting that LMs might internally represent cross-modal conceptual meaning.
Grand Challenge: Mediating Between Confirmatory and Exploratory Research Cultures in Health Sciences and Visual Analytics
Venue: arXiv cs.HC 2025
Year: 2025

Collaboration between health science and visual analytics research is often hindered by different, sometimes incompatible approaches to research design. Health science often follows hypothesis-driven protocols, registered in advance, and focuses on reproducibility and risk mitigation. Visual analytics, in contrast, relies on iterative data exploration, prioritizing insight generation and analytic refinement through user interaction. These differences create challenges in interdisciplinary projects, including misaligned terminology, unrealistic expectations about data readiness, divergent validation norms, or conflicting explainability requirements. To address these persistent tensions, we identify seven research needs and actions: (1) guidelines for broader community adoption, (2) agreement on quality and validation benchmarks, (3) frameworks for aligning research tasks, (4) integrated workflows combining confirmatory and exploratory stages, (5) tools for harmonizing terminology across disciplines, (6) dedicated bridging roles for transdisciplinary work, and (7) cultural adaptation and mutual recognition. We organize these needs in a framework with three areas: culture, standards, and processes. They can constitute a research agenda for developing reliable, reproducible, and clinically relevant data-centric methods.
Copyright Protection for Large Language Models: A Survey of Methods, Challenges, and Trends
Venue: arXiv cs.CR 2025
Year: 2025

Copyright protection for large language models is of critical importance, given their substantial development costs, proprietary value, and potential for misuse. Existing surveys have predominantly focused on techniques for tracing LLM-generated content-namely, text watermarking-while a systematic exploration of methods for protecting the models themselves (i.e., model watermarking and model fingerprinting) remains absent. Moreover, the relationships and distinctions among text watermarking, model watermarking, and model fingerprinting have not been comprehensively clarified. This work presents a comprehensive survey of the current state of LLM copyright protection technologies, with a focus on model fingerprinting, covering the following aspects: (1) clarifying the conceptual connection from text watermarking to model watermarking and fingerprinting, and adopting a unified terminology that incorporates model watermarking into the broader fingerprinting framework; (2) providing an overview and comparison of diverse text watermarking techniques, highlighting cases where such methods can function as model fingerprinting; (3) systematically categorizing and comparing existing model fingerprinting approaches for LLM copyright protection; (4) presenting, for the first time, techniques for fingerprint transfer and fingerprint removal; (5) summarizing evaluation metrics for model fingerprints, including effectiveness, harmlessness, robustness, stealthiness, and reliability; and (6) discussing open challenges and future research directions. This survey aims to offer researchers a thorough understanding of both text watermarking and model fingerprinting technologies in the era of LLMs, thereby fostering further advances in protecting their intellectual property.
A Tight Lower Bound for Doubling Spanners
Venue: arXiv cs.CG 2025
Year: 2025

Any $n$-point set in the $d$-dimensional Euclidean space $\mathbb{R}^d$, for $d = O(1)$, admits a $(1+ε)$-spanner with $\tilde{O}(n \cdot ε^{-d+1})$ edges and lightness $\tilde{O}(ε^{-d})$, for any $ε> 0$. (The {lightness} is a normalized notion of weight, where we divide the spanner weight by the MST weight. The $\tilde{O}$ and $\tildeΩ$ notations hide $\texttt{polylog}(ε^{-1})$ terms.) Moreover, this result is tight: For any $2 \le d = O(1)$, there exists an $n$-point set in $\mathbb{R}^d$, for which any $(1+ε)$-spanner has $\tildeΩ(n \cdot ε^{-d+1})$ edges and lightness $\tildeΩ(n \cdot ε^{-d})$. The upper bounds for Euclidean spanners rely heavily on the spatial property of {cone partitioning} in $\mathbb{R}^d$, which does not seem to extend to the wider family of {doubling metrics}, i.e., metric spaces of constant {doubling dimension}. In doubling metrics, a {simple spanner construction from two decades ago, the {net-tree spanner}}, has $\tilde{O}(n \cdot ε^{-d})$ edges, and it could be transformed into a spanner of lightness $\tilde{O}(n \cdot ε^{-(d+1)})$ by pruning redundant edges. Despite a large body of work, it has remained an open question whether the superior Euclidean bounds of $\tilde{O}(n \cdot ε^{-d+1})$ edges and lightness $\tilde{O}(ε^{-d})$ could be achieved also in doubling metrics. We resolve this question in the negative by presenting a surprisingly simple and tight lower bound, which shows, in particular, that the net-tree spanner and its pruned version are both optimal.
Pushing the Limits of Frequency Analysis in Leakage Abuse Attacks
Venue: arXiv cs.CR 2025
Year: 2025

Searchable encryption (SE) is the most scalable cryptographic primitive for searching on encrypted data. Typical SE constructions often allow access-pattern leakage, revealing which encrypted records are retrieved in the server's responses. All the known generic cryptanalyses assume either that the queries are issued uniformly at random or that the attacker observes the search-pattern leakage. It remains unclear what can be reconstructed when using only the access-pattern leakage and knowledge of the query distribution. In this work, we focus on the cryptanalytic technique of frequency analysis in the context of leakage-abuse attacks on schemes that support encrypted range queries. Frequency analysis matches the frequency of retrieval of an encrypted record with a plaintext value based on its probability of retrieval that follows from the knowledge of the query distribution. We generalize this underexplored cryptanalytic technique and introduce a generic attack framework called Leakage-Abuse via Matching (LAMA) that works even on high-dimensional encrypted data. We identify a parameterization of LAMA that brings frequency analysis to its limit -- that is, we prove that there is no additional frequency matching that an attacker can perform to refine the result. Furthermore, we show that our results hold for any class of convex queries, and not just axis-aligned rectangles, which is the assumption in all other attacks on range schemes. Using these results, we identify query distributions that make frequency analysis challenging for the attacker and, thus, can act as a mitigation mechanism. Finally, we implement and benchmark LAMA and reconstruct, for the first time, plaintext data from encrypted range queries spanning up to four dimensions.
Intelligent Edge Resource Provisioning for Scalable Digital Twins of Autonomous Vehicles
Venue: arXiv cs.NI 2025
Year: 2025

The next generation networks offers significant potential to advance Intelligent Transportation Systems (ITS), particularly through the integration of Digital Twins (DTs). However, ensuring the uninterrupted operation of DTs through efficient computing resource management remains an open challenge. This paper introduces a distributed computing archi tecture that integrates DTs and Mobile Edge Computing (MEC) within a software-defined vehicular networking framework to enable intelligent, low-latency transportation services. A network aware scalable collaborative task provisioning algorithm is de veloped to train an autonomous agent, which is evaluated using a realistic connected autonomous vehicle (CAV) traffic simulation. The proposed framework significantly enhances the robustness and scalability of DT operations by reducing synchronization errors to as low as 5% while achieving up to 99.5% utilization of edge computing resources.
Investigating Sensors and Methods in Grasp State Classification in Agricultural Manipulation
Venue: arXiv cs.RO 2025
Year: 2025

Effective and efficient agricultural manipulation and harvesting depend on accurately understanding the current state of the grasp. The agricultural environment presents unique challenges due to its complexity, clutter, and occlusion. Additionally, fruit is physically attached to the plant, requiring precise separation during harvesting. Selecting appropriate sensors and modeling techniques is critical for obtaining reliable feedback and correctly identifying grasp states. This work investigates a set of key sensors, namely inertial measurement units (IMUs), infrared (IR) reflectance, tension, tactile sensors, and RGB cameras, integrated into a compliant gripper to classify grasp states. We evaluate the individual contribution of each sensor and compare the performance of two widely used classification models: Random Forest and Long Short-Term Memory (LSTM) networks. Our results demonstrate that a Random Forest classifier, trained in a controlled lab environment and tested on real cherry tomato plants, achieved 100% accuracy in identifying slip, grasp failure, and successful picks, marking a substantial improvement over baseline performance. Furthermore, we identify a minimal viable sensor combination, namely IMU and tension sensors that effectively classifies grasp states. This classifier enables the planning of corrective actions based on real-time feedback, thereby enhancing the efficiency and reliability of fruit harvesting operations.
CryptoScope: Utilizing Large Language Models for Automated Cryptographic Logic Vulnerability Detection
Venue: arXiv cs.CR 2025
Year: 2025

Cryptographic algorithms are fundamental to modern security, yet their implementations frequently harbor subtle logic flaws that are hard to detect. We introduce CryptoScope, a novel framework for automated cryptographic vulnerability detection powered by Large Language Models (LLMs). CryptoScope combines Chain-of-Thought (CoT) prompting with Retrieval-Augmented Generation (RAG), guided by a curated cryptographic knowledge base containing over 12,000 entries. We evaluate CryptoScope on LLM-CLVA, a benchmark of 92 cases primarily derived from real-world CVE vulnerabilities, complemented by cryptographic challenges from major Capture The Flag (CTF) competitions and synthetic examples across 11 programming languages. CryptoScope consistently improves performance over strong LLM baselines, boosting DeepSeek-V3 by 11.62%, GPT-4o-mini by 20.28%, and GLM-4-Flash by 28.69%. Additionally, it identifies 9 previously undisclosed flaws in widely used open-source cryptographic projects.
Dataset Creation for Visual Entailment using Generative AI
Venue: arXiv cs.CL 2025
Year: 2025

In this paper we present and validate a new synthetic dataset for training visual entailment models. Existing datasets for visual entailment are small and sparse compared to datasets for textual entailment. Manually creating datasets is labor-intensive. We base our synthetic dataset on the SNLI dataset for textual entailment. We take the premise text from SNLI as input prompts in a generative image model, Stable Diffusion, creating an image to replace each textual premise. We evaluate our dataset both intrinsically and extrinsically. For extrinsic evaluation, we evaluate the validity of the generated images by using them as training data for a visual entailment classifier based on CLIP feature vectors. We find that synthetic training data only leads to a slight drop in quality on SNLI-VE, with an F-score 0.686 compared to 0.703 when trained on real data. We also compare the quality of our generated training data to original training data on another dataset: SICK-VTE. Again, there is only a slight drop in F-score: from 0.400 to 0.384. These results indicate that in settings with data sparsity, synthetic data can be a promising solution for training visual entailment models.
Generative AI in Training and Coaching: Redefining the Design Process of Learning Materials
Venue: arXiv cs.CY 2025
Year: 2025

Generative artificial intelligence (GenAI) is transforming education, redefining the role of trainers and coaches in learning environments. In our study, we explore how AI integrates into the design process of learning materials, assessing its impact on efficiency, pedagogical quality, and the evolving role of human trainers and coaches. Through qualitative interviews with professionals in education and corporate training, we identify the following key topics: trainers and coaches increasingly act as facilitators and content moderators rather than primary creators, efficiency gains allow for a stronger strategic focus but at the same time the new tools require new skills. Additionally, we analyze how the anthropomorphism of AI shapes user trust and expectations. From these insights, we derive how tools based on GenAI can successfully be implemented for trainers and coaches on an individual, organizational, systemic, and strategic level.
Deep Language Geometry: Constructing a Metric Space from LLM Weights
Venue: arXiv cs.CL 2025
Year: 2025

We introduce a novel framework that utilizes the internal weight activations of modern Large Language Models (LLMs) to construct a metric space of languages. Unlike traditional approaches based on hand-crafted linguistic features, our method automatically derives high-dimensional vector representations by computing weight importance scores via an adapted pruning algorithm. Our approach captures intrinsic language characteristics that reflect linguistic phenomena. We validate our approach across diverse datasets and multilingual LLMs, covering 106 languages. The results align well with established linguistic families while also revealing unexpected inter-language connections that may indicate historical contact or language evolution. The source code, computed language latent vectors, and visualization tool are made publicly available at https://github.com/mshamrai/deep-language-geometry.
Adaptive Spiking with Plasticity for Energy Aware Neuromorphic Systems
Venue: arXiv cs.NE 2025
Year: 2025

This paper presents ASPEN, a novel energy-aware technique for neuromorphic systems that could unleash the future of intelligent, always-on, ultra-low-power, and low-burden wearables. Our main research objectives are to explore the feasibility of neuromorphic computing for wearables, identify open research directions, and demonstrate the feasibility of developing an adaptive spiking technique for energy-aware computation, which can be game-changing for resource-constrained devices in always-on applications. As neuromorphic computing systems operate based on spike events, their energy consumption is closely related to spiking activity, i.e., each spike incurs computational and power costs; consequently, minimizing the number of spikes is a critical strategy for operating under constrained energy budgets. To support this goal, ASPEN utilizes stochastic perturbations to the neuronal threshold during training to not only enhance the network's robustness across varying thresholds, which can be controlled at inference time, but also act as a regularizer that improves generalization, reduces spiking activity, and enables energy control without the need for complex retraining or pruning. More specifically, ASPEN adaptively adjusts intrinsic neuronal parameters as a lightweight and scalable technique for dynamic energy control without reconfiguring the entire model. Our evaluation on neuromorphic emulator and hardware shows that ASPEN significantly reduces spike counts and energy consumption while maintaining accuracy comparable to state-of-the-art methods.
Large Language Models in the Data Science Lifecycle: A Systematic Mapping Study
Venue: arXiv cs.CY 2025
Year: 2025

In recent years, Large Language Models (LLMs) have emerged as transformative tools across numerous domains, impacting how professionals approach complex analytical tasks. This systematic mapping study comprehensively examines the application of LLMs throughout the Data Science lifecycle. By analyzing relevant papers from Scopus and IEEE databases, we identify and categorize the types of LLMs being applied, the specific stages and tasks of the data science process they address, and the methodological approaches used for their evaluation. Our analysis includes a detailed examination of evaluation metrics employed across studies and systematically documents both positive contributions and limitations of LLMs when applied to data science workflows. This mapping provides researchers and practitioners with a structured understanding of the current landscape, highlighting trends, gaps, and opportunities for future research in this rapidly evolving intersection of LLMs and data science.
Reclaiming Constitutional Authority of Algorithmic Power
Venue: arXiv cs.CY 2025
Year: 2025

Whether and how to govern AI is no longer a question of technical regulation. It is a question of constitutional authority. Across jurisdictions, algorithmic systems now perform functions once reserved to public institutions: allocating welfare, determining legal status, mediating access to housing, employment, and healthcare. These are not merely administrative operations. They are acts of rule. Yet the dominant models of AI governance fail to confront this reality. The European approach centers on rights-based oversight, presenting its regulatory framework as a principled defense of human dignity. The American model relies on decentralized experimentation, treating fragmentation as a proxy for democratic legitimacy. Both, in different ways, evade the structural question: who authorizes algorithmic power, through what institutions, and on what terms. This Article offers an alternative. Drawing from early modern Reformed political thought, it reconstructs a constitutional framework grounded in covenantal authority and the right of lawful resistance. It argues that algorithmic governance must rest on three principles. First, that all public power must be lawfully delegated through participatory authorization. Second, that authority must be structured across representative communities with the standing to consent, contest, or refuse. Third, that individuals retain a constitutional right to resist systems that impose orthodoxy or erode the domain of conscience. These principles are then operationalized through doctrinal analysis of federalism, nondelegation, compelled speech, and structural accountability. On this view, the legitimacy of algorithmic governance turns not on procedural safeguards or policy design, but on whether it reflects a constitutional order in which power is authorized by the governed, constrained by law, and answerable to those it affects.
Listening with Language Models: Using LLMs to Collect and Interpret Classroom Feedback
Venue: arXiv cs.CY 2025
Year: 2025

Traditional end-of-quarter surveys often fail to provide instructors with timely, detailed, and actionable feedback about their teaching. In this paper, we explore how Large Language Model (LLM)-powered chatbots can reimagine the classroom feedback process by engaging students in reflective, conversational dialogues. Through the design and deployment of a three-part system-PromptDesigner, FeedbackCollector, and FeedbackAnalyzer-we conducted a pilot study across two graduate courses at UC Santa Cruz. Our findings suggest that LLM-based feedback systems offer richer insights, greater contextual relevance, and higher engagement compared to standard survey tools. Instructors valued the system's adaptability, specificity, and ability to support mid-course adjustments, while students appreciated the conversational format and opportunity for elaboration. We conclude by discussing the design implications of using AI to facilitate more meaningful and responsive feedback in higher education.